文章：

差异血清肽组学揭示预测乳腺癌进展的多标志物模型

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression

原文发布日期：27 June 2024

DOI: 10.3390/cancers16132365

类型: Article

开放获取: 是

英文摘要：

Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased ‘omics’ analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers withm/z761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.

摘要翻译： 

本研究旨在评估低分子量血清肽的差异表达如何以高置信度预测乳腺癌进展。我们采用基于液相色谱-串联质谱（LC/MS-MS）的无偏“组学”分析方法，对乳腺癌患者的血清样本进行分析，以识别在I期和III期乳腺癌中差异表达的分子。研究结果通过标准分析流程和基于机器学习的工作流程生成。在标准工作流程中，初步研究发现65种循环生物标志物候选物具有统计学显著的差异表达。后续研究证实了其中部分标志物的差异表达。我们采用探索性算法构建了基于多种生物标志物组合的模型，该算法旨在获得比单一标志物更高的诊断效能和准确性。随后在盲法验证研究中测试了单一生物标志物及更复杂的多标志物模型。多标志物模型在验证研究中保持了预测能力，其中最佳模型的曲线下面积（AUC）达到0.84，灵敏度为43%，特异性为88%。一个质荷比（m/z）为761.38的下调标志物被鉴定为纤维蛋白原α链。基于机器学习的分析生成了一个能够正确分类研究中所有受试者的分类器，并展示了实现高置信度分类器输出所需的参数约束条件。这些结果表明，血清肽生物标志物模型可经过优化，用于临床环境中乳腺癌分期的评估。

原文链接：

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression